Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis

By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena

Fuzzy modeling frequently comes with contradictory specifications: interpretability, that's the aptitude to precise the genuine approach habit in a understandable method, and accuracy, that is the aptitude to faithfully symbolize the genuine procedure. during this framework, essentially the most vital components is linguistic fuzzy modeling, the place the legibility of the received version is the most target. This job is generally built through linguistic (Mamdani) fuzzy rule-based platforms. An energetic learn region is orientated in the direction of using new thoughts and buildings to increase the classical, inflexible linguistic fuzzy modeling with the most goal of accelerating its precision measure. frequently, this accuracy development has been conducted with no contemplating the corresponding interpretability loss. at present, new tendencies were proposed attempting to safeguard the linguistic fuzzy version description energy through the optimization strategy. Written by way of best specialists within the box, this quantity collects a few consultant researcher that pursue this method.

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Heuristic information: Define the way of assigning a heuristic preference to each choice that the ant has to take in each step to generate the solution. 3. Pheromone initialization: Establish an appropriate way of initializing the pheromone. 4. Fitness function: Define a fitness function to be optimized. 5. ACO algorithm: Select an ACO algorithm and apply it to the problem. Fig. 5. 2 Problem Representation For applying ACO in the COR methodology, it is convenient to see it as a combinatorial optimization problem with the capability of being represented on a graph.

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